Comparison of solar‐induced chlorophyll fluorescence, light‐use efficiency, and process‐based <scp>GPP</scp> models in maize

Wagle, Pradeep; Zhang, Yongguang; Cui, Jin; Xiao, Xiangming

doi:10.1890/15-1434

Cited by 89 publications

(67 citation statements)

References 62 publications

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“…SIF is a by-product of the photosynthetic process, thus is mechanistically coupled to vegetation functioning in contrast to optical reflectance-based vegetation indices (VIs), such as the normalized difference vegetation index, which is often used to model or predict productivity [9,[11][12][13][14][15][16][17]. SIF data from satellite platforms have been demonstrated to be related to plant photosynthetic functioning, have been shown to scale linearly with modelled and flux tower GPP, have empirical links with crop and deciduous forest GPP in temperate zones, and captured differences in relative productivity of different vegetative classes in temperate regions [11,12,14,[18][19][20][21][22][23][24][25][26][27][28][29].…”

Section: Introductionmentioning

confidence: 99%

“…Studies also suggest SIF is an earlier indicator of vegetation stress than other remote sensing vegetative health indices [20,[30][31][32][33][34][35][36][37]. Global scale studies utilizing satellite-based SIF measurements and vegetation classes based on the International Geosphere Biosphere Programme (IGBP) classifications have shown significant vegetation class-specific SIF~GPP relationships, except for tropical evergreen broadleaf forest (EBF) [12,15,28,29,[38][39][40][41][42]. Li, Xiao [41], in particular, examined the vegetation class-specific SIF for eight vegetation classes for the entire globe (deciduous broadleaf forest (DBF), EBF, grasslands (GRA), savanna (SAV), evergreen needleleaf forest (ENF), open shrubland (OSH), crop (CRO) and mixed forests (MF)) and found significant differences in SIF among some classes at global scale as well as SIF~GPP relationships for all but EBF [42].…”

Section: Introductionmentioning

confidence: 99%

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Spatiotemporal Patterns and Phenology of Tropical Vegetation Solar-Induced Chlorophyll Fluorescence across Brazilian Biomes Using Satellite Observations

et al. 2019

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Solar-induced fluorescence (SIF) has been empirically linked to gross primary productivity (GPP) in multiple ecosystems and is thus a promising tool to address the current uncertainties in carbon fluxes at ecosystem to continental scales. However, studies utilizing satellite-measured SIF in South America have concentrated on the Amazonian tropical forest, while SIF in other regions and vegetation classes remain uninvestigated. We examined three years of Orbiting Carbon Observatory-2 (OCO-2) SIF data for vegetation classes within and across the six Brazilian biomes (Amazon, Atlantic Forest, Caatinga, Cerrado, Pampa, and Pantanal) to answer the following: (1) how does satellite-measured SIF differ? (2) What is the relationship (strength and direction) of satellite-measured SIF with canopy temperature (T can ), air temperature (T air ), and vapor pressure deficit (VPD)? (3) How does the phenology of satellite-measured SIF (duration and amplitude of seasonal integrated SIF) compare? Our analysis shows that OCO-2 captures a significantly higher mean SIF with lower variability in the Amazon and lower mean SIF with higher variability in the Caatinga compared to other biomes. OCO-2 also distinguishes the mean SIF of vegetation types within biomes, showing that evergreen broadleaf (EBF) mean SIF is significantly higher than other vegetation classes (deciduous broadleaf (DBF), grassland (GRA), savannas (SAV), and woody savannas (WSAV)) in all biomes. We show that the strengths and directions of correlations of OCO-2 mean SIF to T can , T air , and VPD largely cluster by biome: negative in the Caatinga and Cerrado, positive in the Pampa, and no correlations were found in the Pantanal, while results were mixed for the Amazon and Atlantic Forest. We found mean SIF most strongly correlated with VPD in most vegetation classes in most biomes, followed by T can . Seasonality from time series analysis reveals that OCO-2 SIF measurements capture important differences in the seasonal timing of SIF for different classes, details masked when only examining mean SIF differences. We found that OCO-2 captured the highest base integrated SIF and lowest seasonal pulse integrated SIF in the Amazon for all vegetation classes, indicating continuous photosynthetic activity in the Amazon exceeds other biomes, but with small seasonal increases. Surprisingly, Pantanal EBF SIF had the highest total integrated SIF of all classes in all biomes due to a large seasonal pulse. Additionally, the length of seasons only accounts for about 30% of variability in total integrated SIF; thus, integrated SIF is likely captures differences in photosynthetic activity separate from structural differences. Our results show that satellite measurements of SIF can distinguish important functioning and phenological differences in vegetation classes and thus has the potential to improve our understanding of productivity and seasonality in the tropics.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Patterns and Phenology of Tropical Vegetation Solar-Induced Chlorophyll Fluorescence across Brazilian Biomes Using Satellite Observations

et al. 2019

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“…Data obtained from these two sites have been used to study CO 2 exchanges, crop phenology, and gross primary production and respiration (e.g. Verma et al 2005, Suyker et al 2005, Wagle et al 2016. However, no systematic evaluation of irrigation impacts on hydro-meteorological variables has been conducted.…”

Section: Introductionmentioning

confidence: 99%

Memory of irrigation effects on hydroclimate and its modeling challenge

Chen

Barlage

et al. 2018

Environ. Res. Lett.

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Irrigation modifies land-surface water and energy budgets, and also influences weather and climate. However, current earth-system models, used for weather prediction and climate projection, are still in their infancy stage to consider irrigation effects. This study used long-term data collected from two contrasting (irrigated and rainfed) nearby maize-soybean rotation fields, to study the effects of irrigation memory on local hydroclimate. For a 12 year average, irrigation decreases summer surface-air temperature by less than 1 • C and increases surface humidity by 0.52 g kg −1 . The irrigation cooling effect is more pronounced and longer lasting for maize than for soybean. Irrigation reduces maximum, minimum, and averaged temperature over maize by more than 0.5 • C for the first six days after irrigation, but its temperature effect over soybean is mixed and negligible two or three days after irrigation. Irrigation increases near-surface humidity over maize by about 1 g kg −1 up to ten days and increases surface humidity over soybean (∼ 0.8 g kg −1 ) with a similar memory. These differing effects of irrigation memory on temperature and humidity are associated with respective changes in the surface sensible and latent heat fluxes for maize and soybean. These findings highlight great need and challenges for earth-system models to realistically simulate how irrigation effects vary with crop species and with crop growth stages, and to capture complex interactions between agricultural management and water-system components (crop transpiration, precipitation, river, reservoirs, lakes, groundwater, etc.) at various spatial and temporal scales.

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“…Guanter et al, 2014]. SIF captures the seasonal variation of GPP and can be used as a reliable proxy for validating the trend of GPP [Lee et al, 2013;Wagle et al, 2016]. The objective of this HE ET AL.…”

Section: Introductionmentioning

confidence: 99%

Assessment of SMAP soil moisture for global simulation of gross primary production

Chen

Liu

et al. 2017

J. Geophys. Res. Biogeosci.

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In this study, high‐quality soil moisture data derived from the Soil Moisture Active Passive (SMAP) satellite measurements are evaluated from a perspective of improving the estimation of the global gross primary production (GPP) using a process‐based ecosystem model, namely, the Boreal Ecosystem Productivity Simulator (BEPS). The SMAP soil moisture data are assimilated into BEPS using an ensemble Kalman filter. The correlation coefficient (r) between simulated GPP from the sunlit leaves and Sun‐induced chlorophyll fluorescence (SIF) measured by Global Ozone Monitoring Experiment‐2 is used as an indicator to evaluate the performance of the GPP simulation. Areas with SMAP data in low quality (i.e., forests), or with SIF in low magnitude (e.g., deserts), or both are excluded from the analysis. With the assimilated SMAP data, the r value is enhanced for Africa, Asia, and North America by 0.016, 0.013, and 0.013, respectively (p < 0.05). Significant improvement in r appears in single‐cropping agricultural land where the irrigation is not considered in the model but well captured by SMAP (e.g., 0.09 in North America, p < 0.05). With the assimilation of SMAP, areas with weak model performances are identified in double or triple cropping cropland (e.g., part of North China Plain) and/or mountainous area (e.g., Spain and Turkey). The correlation coefficient is enhanced by 0.01 in global average for shrub, grass, and cropland. This enhancement is small and insignificant because nonwater‐stressed areas are included.

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Comparison of solar‐induced chlorophyll fluorescence, light‐use efficiency, and process‐based GPP models in maize

Cited by 89 publications

References 62 publications

Spatiotemporal Patterns and Phenology of Tropical Vegetation Solar-Induced Chlorophyll Fluorescence across Brazilian Biomes Using Satellite Observations

Spatiotemporal Patterns and Phenology of Tropical Vegetation Solar-Induced Chlorophyll Fluorescence across Brazilian Biomes Using Satellite Observations

Memory of irrigation effects on hydroclimate and its modeling challenge

Assessment of SMAP soil moisture for global simulation of gross primary production

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